The increasing connectivity and data exchange between vehicles and the cloud have led to growing privacy concerns. To keep on gaining product insights through data collection while guaranteeing privacy protection, an anonymization-by-design approach should be used. A rising number of anonymization methods, not limited to the automotive domain, can be found in the literature and practice. The developers need support to select the suitable anonymization technique. To this end, we make the following two contributions: 1) We apply our knowledge from the automotive domain to outline the usage of qualitative metrics for anonymization techniques assessment; 2) We introduce HySAAD, a hybrid selection approach for anonymization by design that leverages this groundwork by recommending appropriate anonymization techniques for each mobile data analytics use case based on both, qualitative (i.e., "soft") metrics and quantitative (i.e., "hard") metrics. Using a real-world use case from the automotive, we demonstrate the applicability and effectiveness of HySAAD.

Nowadays, the amount of data is growing rapidly. Through data mining and analysis, information and knowledge can be derived based on this growing volume of data. Different tools have been introduced in the past to specify data analysis scenarios in a graphical manner, for instance, PowerBI, Knime, or RapidMiner. However, when it comes to specifying complex data analysis scenarios, e.g., in larger companies, domain experts can easily become overwhelmed by the extensive functionality and configuration possibilities of these tools. In addition, the tools vary significantly regarding their powerfulness and functionality, which could lead to the need to use different tools for the same scenario. In this demo paper, we introduce our novel user-centric interactive data mashup tool that supports domain experts in interactively creating their analysis scenarios and introduces essential functionalities that are lacking in similar tools, such as direct feedback of data quality issues or recommendation of suitable data sources not yet considered.

Today, a multitude of highly-connected applications and information systems hold, consume and produce huge amounts of heterogeneous data. The overall amount of data is even expected to dramatically increase in the future. In order to conduct, e.g., data analysis, visualizations or other value-adding scenarios, it is necessary to integrate specific, relevant parts of data into a common source. Due to oftentimes changing environments and dynamic requests, this integration has to support ad-hoc and flexible data processing capabilities. Furthermore, an iterative and explorative trial-and-error integration based on different data sources has to be possible. To cope with these requirements, several data mashup platforms have been developed in the past. However, existing solutions are mostly non-extensible, monolithic systems or applications with many limitations regarding the mentioned requirements. In this paper, we introduce an approach that copes with these issues (i) by the introduction of patterns to enable decoupling from implementation details, (ii) by a cloud-ready approach to enable availability and scalability, and (iii) by a high degree of flexibility and extensibility that enables the integration of heterogeneous data as well as dynamic (un-)tethering of data sources. We evaluate our approach using runtime measurements of our prototypical implementation.

The automation of business processes is of vital importance for organizations to speed up their business and to lower costs. Due to emerging technologies in the field of Internet of Things, changing situations can be recognized automatically, which provides the basis for an automated adaptation of process executions in order to react to changing circumstances. Although approaches exist that enable creating self-adapting workflows, a systematic modelling approach that supports the specification of situational dependencies directly in workflow models is missing. In this paper, we tackle this issue by presenting a modelling extension called SitME that defines (i) an extensible Situation Event type, (ii) the concept of Situational Scopes, and (iii) a visual notation. As the introduced extension is language-independent, we apply the approach to BPEL to validate its practical feasibility.